USCOTS 2011 - CAUSEmos Student Attitudes

Students' Perceptions of Statistics

Marjorie Bond, Monmouth College

In order to improve students' attitudes toward statistics, we should know what are their perceptions of statistics. Pilot data has shown that students will use the word "statistics" when descibing what they expect to learn and that most are not surprised at what they are learning. This poster presents additional pilot data collected Spring 2011 with an improved survey, connects the students' responses with the categories given in Reid and Petocz, 2002, article in JSE, and describes future research. This research is part of the statistics attitude research cluter group formed at USCOTS 2009.

Assessing Changes in Students' Attitudes: The Good, the Bad, and the Ugly

Michelle Millar, Mount Saint Vincent University
Candace Schau, CS Consultants, LLC

Many statistics instructors and statistics education researchers are interested in how students' attitudes change across statistics courses from beginning (pretest) to end (posttest). There are a variety of types of scores and analysis methods used to assess pretest-posttest change. These include, for example, t procedures on gain scores to estimate the mean gain, extension of such methods to allow for dependence in the data, and linear models to estimate the mean gain conditional on the pre-score. We evaluate some of the most popular analysis methods to compare the results in terms of statistical significance, effect sizes, and confidence intervals, and discuss the merits of more complex methods. Although we use component scores from the Survey of Attitudes toward Statistics(c) for our evaluation, our work applies to other types of student outcomes including, for example, achievement.

The Importance of Students' Attitudes Toward Statistics(c) in Statistics Courses

Candace Schau, CS Consultants, LLC
Esma Emmioglu, Middle East Technical University
Caroline Ramirez, University of California - Davis

Our poster is based on models developed in educational psychology that suggest that students' attitudes and motivations are more important than course achievement in their academic and life choices and behaviors. Applying Eccles' and colleagues Expectancy-Value Model (Eccles & Wigfield, 2002) and the numerous research findings that support it (i.e., Durik, Vida & Eccles, 2006) to statistics education, we developed our Model of Students' Attitudes and Motivations toward Statistics (SAMS); see Figure 1. The SAMS Model proposes that high school and post-secondary students who have positive attitudes toward statistics are more likely to expend the effort needed to understand statistics. The effort leads to better course outcomes, including achievement, as well as appropriate use of statistics in their professional and daily lives. The SAMS model also proposes that student characteristics and previous academic achievement impact students' attitudes toward statistics and course outcomes.

Figure1. Students' Attitudes and Motivations toward Statistics (SAMS) Model

The Survey of Attitudes toward Statistics(c) assesses the constructs contained in the SAMS Model; results from international studies using the SATS(c) support the predictions in our Model. These studies have shown that previous achivement and student characteristics have roles in explaining students' attitudes toward statistics and statistics outcomes (e.g., Chiesi & Primi, 2010; Nasser, 2004; Sorge, 2001). In addition, students' attitudes toward statistics impact students' statistics outcomes (e.g., Bude et al. 2007; Verhoeven, 2009; Tempelaar et al., 2007).

Our SAMS Model and the findings supporting it suggest that statistics instructors should

1. Include developing positive attitudes and motivations as a course objective,
2. Assess changes in students' attitudes across their courses,
3. Consider factors that affect students' attitudes toward statistics and their statistics outcomes, as well as how these relationships work, and
4. Include changes in students' attitudes as part of evaluating instruction.

References:

Bude, L., Van de Wiel, M. W. J., Imbos, T., Candel, M. J. J. M., Broers, N. J., and Berger, M. P. F. (2007), "Students' Achievements in a Statistics Course in Relation to Motivational Aspects and Study Behavior", Statistics Education Research Journal, 6, 5-21.

Chiesi, F., and Primi, C. (2010). "Cognitive and Non-cognitive Factors Related to Students' Statistics Achievement", Statistics Education Research Journal, 9, 6-26.

Durik, A. M., Vida, M., and Eccles, J. S. (2006), "Task Values and Ability Beliefs as Predictors of High School Literacy Choices: A Developmental Analysis", Journal of Educational Psychology, 98, 382-393.

Eccles, J. S., and Wigfield, A. (2002), "Motivational Beliefs, Values and Goals", Annual Review of Psychology, 53, 109-132.

Nasser, F. M. (2004). Structural Model of the Effects of Cognitive and Affective Factors on the Achievement of Arabic-Speaking Pre-Service Teachers in Introductory Statistics. Journal of Statistics Education [online], 12, 1. Available at http://www.amstat.org/publications/jse/v12n1/nasser.html.

Sorge, C. (2001), "Impact of Engineering Students' Attitudes on Achievement in Statistics: A Structural Equation Model Analysis", unpublished Ph.D. dissertation, The University of New Mexico, Department of Educational Psychology.

Tempelaar, D. T., Schim van der Loeff, S. and Gijselaers, W. H. (2007), "A Structural Equation Model Analyzing the Relationship of Students' Attitudes Toward Statistics, Prior Reasoning Abilities and Course Performance", Statistics Education Research Journal [on-line], 6, 78-102. Available at www.stat.auckland.ac.nz/serj.

Verhoeven, P. S. (2009), "Quality in Statistics Education. Determinants of Course Outcomes in Methods and Statistics Education at Universities and Colleges" The Hague: Boom Onderwijs.

Instructor Characteristics Associated with Improved Student Attitudes Toward Statistics

Phil Yates, Saint Michael's College
Michael Posner, Villanova University

Statistics is one of the most common and important courses taken at college. National and international discussions on the content in the course abound, but less attention is paid to and research done on student attitudes. Students left with poor attitudes towards the importance of statistics and its utility in today's data-driven society are left with a bad taste in their mouths, which impacts them throughout their collegiate and professional careers. Endeavors to increase the perception of statistics and student attitudes are crucially important. The Survey of Attitudes Toward Statistics (SATS) was developed to help instructors and researchers evaluate student attitudes and examine their relationships with teaching and learning outcomes in the classroom. Along with student attitudes, various instructor characteristics were gathered from the SATS data warehouse and examined to determine which traits are most associated with increases in students' attitudes toward statistics. The analyses employ hierarchical linear models that incorporate the nested nature of the data. Results show that students of instructors with a highest degree in statistics like statistics more than when they started, don't think statistics is as hard as they expected, and show an increased interest in statistics, relative to instructors with backgrounds in other fields, with those in mathematics performing the worst. These results are consistent with opinions of leaders in the field of statistics education and support the need for future research on how to minimize the performance gap in terms of changes in student attitudes between teachers with different experiences and backgrounds.